Large Language Model
Large language models (LLMs) are sophisticated AI systems designed to process and generate human-like text, aiming to improve various natural language processing tasks. Current research focuses on enhancing LLM safety, efficiency (through techniques like quantization and optimized decoding), and fairness, as well as improving their ability to perform complex reasoning and handle diverse instructions. These advancements are significant because they address critical limitations in current LLMs and pave the way for broader applications across diverse fields, including healthcare, legal tech, and autonomous systems.
Papers
Dynamic Intelligence Assessment: Benchmarking LLMs on the Road to AGI with a Focus on Model Confidence
Norbert Tihanyi, Tamas Bisztray, Richard A. Dubniczky, Rebeka Toth, Bertalan Borsos, Bilel Cherif, Mohamed Amine Ferrag, Lajos Muzsai, Ridhi Jain, Ryan Marinelli, Lucas C. Cordeiro, Merouane Debbah
"What is the value of {templates}?" Rethinking Document Information Extraction Datasets for LLMs
Ran Zmigrod, Pranav Shetty, Mathieu Sibue, Zhiqiang Ma, Armineh Nourbakhsh, Xiaomo Liu, Manuela Veloso
Keep Guessing? When Considering Inference Scaling, Mind the Baselines
Gal Yona, Or Honovich, Omer Levy, Roee Aharoni
Hallucination Detox: Sensitive Neuron Dropout (SeND) for Large Language Model Training
Shahrad Mohammadzadeh, Juan David Guerra, Marco Bonizzato, Reihaneh Rabbany, Golnoosh Farnadi
Evaluating Consistencies in LLM responses through a Semantic Clustering of Question Answering
Yanggyu Lee, Jihie Kim
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs
Xin Zhou, Ping Nie, Yiwen Guo, Haojie Wei, Zhanqiu Zhang, Pasquale Minervini, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang
A Comprehensive Evaluation of Cognitive Biases in LLMs
Simon Malberg, Roman Poletukhin, Carolin M. Schuster, Georg Groh
CalibraEval: Calibrating Prediction Distribution to Mitigate Selection Bias in LLMs-as-Judges
Haitao Li, Junjie Chen, Qingyao Ai, Zhumin Chu, Yujia Zhou, Qian Dong, Yiqun Liu
A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice
Hsiu-Yuan Huang, Yutong Yang, Zhaoxi Zhang, Sanwoo Lee, Yunfang Wu
Who is Undercover? Guiding LLMs to Explore Multi-Perspective Team Tactic in the Game
Ruiqi Dong, Zhixuan Liao, Guangwei Lai, Yuhan Ma, Danni Ma, Chenyou Fan
Contextual Augmented Multi-Model Programming (CAMP): A Hybrid Local-Cloud Copilot Framework
Yuchen Wang, Shangxin Guo, Chee Wei Tan
When Machine Unlearning Meets Retrieval-Augmented Generation (RAG): Keep Secret or Forget Knowledge?
Shang Wang, Tianqing Zhu, Dayong Ye, Wanlei Zhou
HyQE: Ranking Contexts with Hypothetical Query Embeddings
Weichao Zhou, Jiaxin Zhang, Hilaf Hasson, Anu Singh, Wenchao Li
Lossless KV Cache Compression to 2%
Zhen Yang, J.N.Han, Kan Wu, Ruobing Xie, An Wang, Xingwu Sun, Zhanhui Kang
A Prompt Refinement-based Large Language Model for Metro Passenger Flow Forecasting under Delay Conditions
Ping Huang, Yuxin He, Hao Wang, Jingjing Chen, Qin Luo
A Prompt Engineering Approach and a Knowledge Graph based Framework for Tackling Legal Implications of Large Language Model Answers
George Hannah, Rita T. Sousa, Ioannis Dasoulas, Claudia d'Amato
Are LLMs Good Zero-Shot Fallacy Classifiers?
Fengjun Pan, Xiaobao Wu, Zongrui Li, Anh Tuan Luu
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model
Jiahao Wang, Amer Shalaby
CAP: Data Contamination Detection via Consistency Amplification
Yi Zhao, Jing Li, Linyi Yang